대용량 부유식 해상풍력발전단지의 연계선로 거리에 따른 경제성 분석

Offshore wind energy development along the East Coast of the US is proceeding quickly as a result of large areas with an excellent wind resource, low water depths and proximity to large electricity markets. Careful planning of wind turbine deployments in these offshore wind energy lease areas (LA) is required to maximize power output and to minimize wake losses between neighboring wind farms as well as those internal to each wind farm. Here, we used microscale wind modeling with two wake parameterizations to evaluate the potential annual energy production (AEP) and wake losses in the different LA areas, and we developed and applied a levelized cost of energy (LCoE) model to quantify the impact of different wind turbine layouts on LCoE. The modeling illustrated that if the current suite of LA is subject to deployment of 15 MW wind turbines at a spacing of 1.85 km, they will generate 4 to 4.6% of total national electricity demand. The LCoE ranged from $68 to $102/MWh depending on the precise layout selected, which is cost competitive with many other generation sources. The scale of the wind farms that will be deployed greatly exceed those currently operating and mean that wake-induced power losses are considerable but still relatively poorly constrained. AEP and LCoE exhibited significant dependence on the precise wake model applied. For the largest LA, the AEP differed by over 10% depending on the wake model used, leading to a $10/MWh difference in LCoE for the wind turbine layout with 1.85 km spacing.

This article presents a framework to integrate and optimize the design of large-scale wind turbines. Annual energy production, load analysis, the structural design of components and the wind farm operation model are coupled to perform a system-level nonlinear optimization. As well as the commonly used design objective levelized cost of energy (LCoE), key metrics of engineering economics such as net present value (NPV), internal rate of return (IRR) and the discounted payback time (DPT) are calculated and used as design objectives, respectively. The results show that IRR and DPT have the same effect as LCoE since they all lead to minimization of the ratio of the capital expenditure to the energy production. Meanwhile, the optimization for NPV tends to maximize the margin between incomes and costs. These two types of economic metrics provide the minimal blade length and maximal blade length of an optimal blade for a target wind turbine at a given wind farm. The turbine properties with respect to the blade length and tower height are also examined. The blade obtained with economic optimization objectives has a much larger relative thickness and smaller chord distributions than that obtained for high aerodynamic performance design. Furthermore, the use of cost control objectives in optimization is crucial in improving the economic efficiency of wind turbines and sacrificing some aerodynamic performance can bring significant reductions in design loads and turbine costs.

Floating offshore wind, far less constrained by water depths than bottom-fixed, has great potential in the Mediterranean Basin. The levelised cost of energy (LCOE) is arguably the single most reliable metric to measure the viability of energy projects. In this work, the levelised cost of energy for floating offshore wind is mapped for the first time in the European and Eastern Mediterranean with a detailed cost breakdown and a focus on semi-submersible platforms. A multi-parameter analysis is conducted in a case study to discern the effects of specific elements on the LCOE. Expressions are formulated as functions of site-specific variables: distance to shore, water depth and annual energy production. The latter is estimated accurately by combining the power curve of an exemplar wind turbine with hindcast, site-specific wind data. The mapping shows the paramount importance of the wind climate, i.e., the resource, for the LCOE. The lowest values (~95 €/MWh) occur where the wind resource is most abundant, i.e., the Gulf of Lion and the Aegean Sea. The highest values (>250 €/MWh) are found where the resource is scarce, i.e., around the Balearic Islands and in the North Adriatic, Tyrrhenian and Levantine Seas. Moderate values of the LCOE (130–180 €/MWh) occur off South Spain (Alboran Sea), Sardinia, Sicily and Malta, and in the South Adriatic. In addition to the local wind resource, other parameters that play a relevant role in the LCOE are those related to the production of energy (number of turbines in the wind farm and installed power), on the one hand, and to substantial sources of costs (cost of turbines and substructure), on the other, as well as the project lifetime and discount rate. These results identify hotspots for the deployment of floating offshore wind in the Mediterranean and opportunities for cost reductions, and contribute to decision-making in a region much in need of renewable energy.

<strong class="journal-contentHeaderColor">Abstract.</strong> Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier-Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed. This inflow model is well suited for RANS simulations of large wind farms because it does not lead to the development of nonphysical wind farm wakes. Second, a RANS-based wind farm parametrization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation less than 1 % in terms of wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 74.4 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models; namely 89.3 % and 99.9 %, for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model (WRF), but the models are in agreement with the inflow wind speed of the downstream wind farm. The double wind farm case is also simulated with the TurbOPark engineering wake model. Similar wake shapes are reproduced by TurbOPark but the model predicts a larger wind farm wake magnitude compared to RANS and WRF. TurbOPark predicts much better results when its ground model is switched off and a wake expansion coefficient of 0.06 is used. The RANS-AD-AWF model is also validated with SCADA measurements in terms of wind farm shape; the model captures the trend of the measurements for a wide range of wind directions, although the SCADA measurements indicate more pronounced wind farm wake shapes for certain wind directions.

Optimization of the Levelized Cost of Energy (LCoE) in wind farms helps ensure profitability and competitiveness of the project. Recent work has explored driving down LCoE by allowing multiple wind turbines in a single wind farm - with different hub heights, rotor diameters, and rated powers. In this work, we performed optimization of the Lillgrund wind farm with continuously varying hub-heights to mitigate wake interference, improve annual energy production (AEP) and reduce LCoE. The optimization converged to a configuration where the turbines were vertically staggered, resulting in an improvement in both AEP and internal rate of return (IRR) - a financial metric related to LCoE. Reducing the number of turbines to a discrete set of 2 or 3 retained nearly all the benefits of staggering but is more aligned with limitations related to manufacturing and logistics.

Techno-economic analysis of high potential offshore wind farm locations in Turkey

Background: During the whole life cycle of an offshore wind farm, from cradle to grave, logistics is needed. Particularly during wind turbine construction, vessels are intensively used. However, no study has been found to evaluate logistics costs during this particular turbine construction phase. Objective: In order to close this gap, this paper aims to propose an Offshore Logistics Cost (OLC) framework, evaluate OLC impact on total cost and identify recommendations to reduce this impact. Methodology: This study considers two quantitative approaches to evaluate OLC share. First quantitative approach is based on cost information found in twenty related studies from literature. Second quantitative approach was developed in two steps. A first step is a bottom up calculation model. This model performs OLC calculations for each scenario identified. In a second step, OLC results are used as input to two existing calculation models: National Renewable Energy Laboratory (NREL) simplified model and commonly accepted model developed by Megavind. Variance of results are then analyzed to determine a representative range for OLC impact on total cost. Results: Using some related values from literature review, this study assesses wind turbine construction offshore logistics to represent around 1.2% of Levelized Cost of Energy (LCOE). Using bottom up approach, this study evaluates offshore logistics during wind turbine construction with a broader contribution, conservatively from 0.6 to 7.6% of LCOE for offshore wind turbines over 4 MW power rating. It is also demonstrated that the higher the wind turbine size, the lower offshore logistics impact on LCOE. Conclusion: Offshore wind logistics costs during construction phase can represent between 0.6 and 7.6% of LCOE and it appears necessary to optimize these costs to contribute to a competitive LCOE. As main offshore logistics cost drivers have been identified, areas to reduce LCOE impact are suggested: increasing weather limits to reduce waiting on weather costs, minimizing work offshore, improving processes, using economy of scale and optimizing vessel use.

This study investigates the effect of wake losses on the Annual Energy Production at the Norwegian offshore wind site Vestavind F . Three different scenarios of wind farm layouts consisting of turbines of the 5 MW, the 10 MW, and the 15 MW class, respectively, are analysed. Eleven years of historical wind data produced by the Norwegian reanalysis wind dataset NORA3 are used as a time-domain input to compute both the wind farm’s power production and the associated production losses due to wake flow interaction. The computations show a substantial year-to-year variation in both wake losses and energy production. The annual wake losses show an inverse relation to the annual average wind speeds and power production. The total wake losses of the 10 MW farm layout show large year-to-year variations from a minimum of 0.53 % to a maximum of 15.60 % of the total production. These significant fluctuations are primarily attributed to substantial differences in annual average wind speeds at the investigated location. Furthermore, the thrust coefficients and rated wind speeds of the turbine types are seen to influence the amount of wake losses. This study highlights the necessity to use meteorological input data of one decade or more to predict future Norwegian wind farms fluctuating energy production and their associated wake losses.

This study compares the performance of two wind farm sites located in Northern Europe: an onshore site and an offshore area in the eastern Baltic Sea region. This study investigates the optimisation of wind farm performance within a fixed project area by maximising annual energy production (AEP) and increasing energy density. Three wake-loss scenarios (≤10%, ≤15%, and ≤20%) were examined to assess the sensitivity of layout optimisation to aerodynamic interaction constraints. Several layout configurations were analysed to reduce wake losses and enhance overall energy output. Wind conditions were assessed using NORA3 reanalysis data, and wake interactions were modelled using the Jensen wake model to estimate AEP. Both wind farms were further compared across key criteria, including cost, power generation efficiency, installation and maintenance requirements, and site availability. Offshore wind farms achieve 1.5–1.7 times higher energy density under similar spatial conditions. However, offshore levelised cost of energy (LCOE) remains roughly 25% higher due to higher capital and infrastructure costs, while onshore LCOE demonstrates better economic performance, driven by lower CAPEX and O&amp;M expenses. The findings highlight the trade-offs between cost efficiency and wake-driven energy performance for onshore and offshore wind development in the eastern Baltic Sea region.

This study investigates the economic, technical, and environmental impacts of integrating a battery energy storage system (BESS) with an offshore wind farm near Koko Sea Port, Nigeria. As renewable energy curtailment increases and Nigeria’s energy demand grows, energy storage systems (ESS) are essential for ensuring a firm power supply. While offshore wind farms have advanced globally, Nigeria remains underrepresented in related research. This study addresses this gap by presenting a comprehensive techno-economic and environmental assessment (TEEA) of an ESS-integrated wind farm along Nigeria's coast. The system includes 477 Siemens Gamesa SG 2.1–114 turbines, producing 930 GWh annually with an instantaneous output of 0.35 GW. Vanadium Redox Flow Batteries (VRFB) were identified as the most suitable ESS, providing 528 MWh of storage to maintain power during wind variability. Economic analysis indicates a Levelized Cost of Energy (LCOE) of 393.35 $/MWh, comparable to global 2020 benchmarks for newly commissioned offshore wind farms. Environmental analysis reveals significant carbon savings, with emissions reduced by over 107% and 74% relative to coal and gas power plants. This study highlights the potential of battery-integrated offshore wind farms to advance Nigeria’s clean energy transition, offering insights for policymakers and stakeholders pursuing sustainable development and Paris Agreement commitments.

Simulations are conducted using a microscale model framework to quantify differences in projected Annual Energy Production (AEP), Capacity Factor (CF) and wake losses for large offshore wind farms that arise due to different input datasets, installed capacity density (ICD) and/or wake parameterizations. Differences in CF (and AEP) and wake losses that arise due to the selection of the wake parameterization have the same magnitude as varying the ICD within the likely range of 2–9 MW km−2. CF simulated with most wake parameterizations have a near-linear relationship with ICD in this range, and the slope of the dependency on ICD is similar to that in mesoscale simulations with the Weather Research and Forecasting (WRF) model. Microscale simulations show that remotely generated wakes can double AEP losses in individual lease areas (LA) within a large LA cluster. Finally, simulations with the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model are shown to differ in terms of wake-induced AEP reduction from those with the WRF model by up to 5%, but this difference is smaller than differences in CF caused by the wind farm parameterization used in the mesoscale modeling. Enhanced evaluation of mesoscale and microscale wake parameterizations against observations of climatological representative AEP and time-varying power production from wind farm Supervisory Control and Data Acquisition (SCADA) data remains critical to improving the accuracy of predictive AEP modeling for large offshore wind farms.

The South Korean government is encouraging the active participation of power generation companies in the offshore wind power project by announcing the renewable energy certificates (REC) weighting plan. However, from a long-term perspective, the offshore wind power must be able to generate profits without government support to demonstrate its business feasibility and attract the voluntary participation of power generation companies. This is because government support may be subject to change, depending on the internal and external political circumstances of the country. This report calculates the expected costs for a 495 MW floating offshore wind farm in South Korea’s market environment and examines how the feasibility of the project shifts depending on the country’s current REC weights. Furthermore, this study intends to determine whether floating offshore wind power can generate profits without the Korean government’s support by calculating the expected profit in combination with the green hydrogen project. The net present value, levelized cost of energy and internal rate of return (IRR) indexes are calculated according to the project’s specific particularities, such as power purchase agreement, REC weighting, distance from shore and sea depth. Based on this, an index-based comparison is revealed and the margin for profitability for such an investment is discussed. The calculation results revealed that with a decrease in capital expenditures and operating expenditure or an increase in the system marginal price under specific assumptions, the value of IRR increased.

The electrical systems needed for offshore wind farms to collect power from wind turbines-and transmit it to shore- will be a significant cost element of these systems. This paper describes the development of a simplified model of the cost and performance of such systems. The performance prediction accounts for losses as a function of the power produced in the wind farm and the length and size of the cables. The cost prediction is flexibly formulated so wind farm configurations can be evaluated by parameters such as the number of wind turbines, wind turbine size, turbine array configuration and spacing, and distance from shore. The collection system-the medium-voltage electrical grid within the wind farm, and the transmission system-the high-voltage electrical connection to an on-shore transmission line-are treated independently in the model. Data sources for the model and limitations of the data are discussed, and comparison is made to costs reported by others. The choice of transmission system technology is also addressed. This electrical system model is intended for integration into a more comprehensive model of offshore wind farm design, cost, and performance that will be used for parametric studies and optimization of wind farm configurations. Because some concepts for future offshore wind installations in deep water use floating platforms, this paper briefly discusses the application of submarine cable technology to nonfixed termination points, a departure from current practice. Introduction The National Wind Technology Center (NWTC) of the National Renewable Energy Laboratory (NREL) in Golden, Colorado, has undertaken a series of concept studies to evaluate the cost and performance of offshore wind farms. The product of these studies will be a comprehensive model of offshore wind farm design, cost, and performance suitable for parametric studies and optimization of wind farmconfigurations. The overall goal of this effort is to helpidentify technology pathways for offshore wind energy development and deployment in the United States. Offshore wind farms present an attractive option because they allow for larger wind turbines that operate in higher wind resources than land-based [1,2]. Offshore installations are also more expensive, so an understanding of their performance, cost, and optimal configurations is needed. This paper is an overview of one of these concept studies. It focuses on the power losses in wind farm electrical power collection and transmission systems, as well as the costs of the system components and their installation. A hypothetical system was based loosely on the Horns Rev offshore wind farm in Denmark. Inquiries were made with manufacturers about electrical and cost data on the required components, which were then compiled in a spreadsheet model. The performance prediction accounts for losses as a function of power output of the wind farm and length and size of the cables. The cost prediction is flexibly formulated so wind farm configurations can be evaluated by parameters such as the number of wind turbines, wind turbine size, turbine array configuration and spacing, and distance from shore.

Unravelling historical cost developments of offshore wind energy in Europe

Wind energy is a promising alternate energy resource. However, the on-land wind farms are limited by space, noise, and visual pollution and, therefore, many countries build wind farms near the shore. Until now, most offshore wind farms have been built in relatively shallow water (less than 30 m) with fixed tower type wind turbines. Recently, several countries have planned to move wind farms to deep water offshore locations to find stronger and steadier wind fields as compared to near shore locations. For the wind farms in deeper water, floating platforms have been proposed to support the wind turbine. The model tests described in this paper were performed at MARIN (maritime research institute netherlands) with a model setup corresponding to a 1:50 Froude scaling. The wind turbine was a scaled model of the national renewable energy lab (NREL) 5 MW horizontal axis reference wind turbine supported by three different generic floating platforms: a spar, a semisubmersible, and a tension-leg platform (TLP). The wave environment used in the tests is representative of the offshore in the state of Maine. In order to capture coupling between the floating platform and the wind turbine, the 1st bending mode of the turbine tower was also modeled. The main purpose of the model tests was to generate data on coupled motions and loads between the three floating platforms and the same wind turbine for the operational, design, and survival seas states. The data are to be used for the calibration and improvement of the existing design analysis and performance numerical codes. An additional objective of the model tests was to establish the advantages and disadvantages among the three floating platform concepts on the basis of the test data. The paper gives details of the scaled model wind turbine and floating platforms, the setup configurations, and the instrumentation to measure motions, accelerations, and loads along with the wind turbine rpm, torque, and thrust for the three floating wind turbines. The data and data analysis results are discussed in the work of Goupee et al. (2012, “Experimental Comparison of Three Floating Wind Turbine Concepts,” OMAE 2012-83645).